Explain your favourite ML algorithm in less than a minute
Question
What’s your favorite algorithm, and can you explain it to me in less than a minute?
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Machine Learning
55 years
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Answers ( 4 )
linear regression is used to model relationship between a single( or multiple) independent variables and a target variable
Logistic Regression is one algorithm used in many classification problems.
Like should a loan be given to a particular customer or not, will the customer
default on the given credit or not.
The output of the algorithm is a probability value which ranges from 0 to 1 for all the
records on the test set and depending on the business problem you are solving, you need
to decide the cut-off value for these probabilities, to categorise your cases in either class.
By default, the cut-off value is 0.5. So, predictions above 0.5 will be categorised as 1 and below 0.5 will
be categorised as 0.
Logistic Regression is used in many classification problems.
Like should student will pass or not. It will rain/not rain.
K-Means Clustering : It is a unsupervised Machine learning algorithm which helps to understand the structure of the data by dividing homogeneous datapoints into subgroups based on pattern using distance method.
Linear regression is one algorithm that is used for regression problems. It is most basic algorithm. It is used in when we want numeric output like in case if we want to predict the amount of rain (in mm) this year based on data of previous years. It simply fits a line through data y=mx+c and based on this makes prediction for new data points where they lie on the line.